class_docstr: Augmenter that leverage contextual word embeddings to find top n similar word for augmentation.
  
  :param str model_dir: Directory of model. It is generated from train_lambada.sh under scritps folders.n
  :param float threshold: The threshold of classification probabilty for accpeting generated text. Return all result if threshold
      is None.
  :param int batch_size: Batch size.
  :param int min_length: The min length of output text.
  :param int max_length: The max length of output text.
  :param float temperature: The value used to module the next token probabilities.
  :param int top_k: The number of highest probability vocabulary tokens to keep for top-k-filtering.
  :param float top_p: If set to float < 1, only the most probable tokens with probabilities that add up to `top_p` or
      higher are kept for generation.
  :param float repetition_penalty : The parameter for repetition penalty. 1.0 means no penalty.
  :param str device: Default value is CPU. If value is CPU, it uses CPU for processing. If value is CUDA, it uses GPU
      for processing. Possible values include 'cuda' and 'cpu'. 
  :param bool force_reload: Force reload the contextual word embeddings model to memory when initialize the class.
      Default value is False and suggesting to keep it as False if performance is the consideration.
  :param str name: Name of this augmenter
  
  >>> import nlpaug.augmenter.sentence as nas
  >>> aug = nas.LambadaAug()
code: def insert(self, data, n=10):
      if not data:
          return data
  
      if isinstance(data, list):
          all_data = data
      else:
          if data.strip() == "":
              return data
          all_data = [data]
  
      for d in all_data:
          if d not in self.label2id:
              raise Exception(
                  "Label {} does not exist. Possible labels are {}".format(
                      d, self.label2id.keys()
                  )
              )
  
      return self.model.predict(all_data, n=n)
  
file_path: nlpaug/nlpaug/augmenter/sentence/lambada.py
imports: import os
  import json
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.lang_models
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 2 for calling the LambadaAug obj's underlying member method code with example usage: obj.insert(data, n) and returns: self, data, data